Evaluation of salivary urea nitrogen test in cats with lower urinary tract outflow obstructions.
Bibliographic record
Abstract
Objective: To determine if the Kidney-Chek salivary test (SN Biomedical) could accurately detect azotemia in cats with urethral obstruction. Animals: Cats included in the study were adult male cats > 1 y of age, each with an inexpressible bladder > 4 cm in diameter as visualized on ultrasound examination. Procedure: Twenty-one cats meeting these criteria were admitted to a veterinary hospital. Each cat had salivary urea nitrogen (SUN) assessed with a Kidney-Chek test, following the Kidney-Chek protocol. Blood urea nitrogen (BUN) and creatinine were also assessed. Results: Twenty-one cats were included in the study. One cat was excluded due to dry saliva sample collection. Final results showed that the SUN test strips were 84% accurate in identifying azotemia or non-azotemia. Azotemia was correctly identified in 78.57% of cats and non-azotemia was correctly identified in 90.9% of cats. Positive correlations were noted between SUN and BUN and SUN and creatinine. There was a negative correlation between SUN and urine specific gravity. No correlation was identified between hydration level and SUN. Hydration had no effect on test results. Conclusion: Salivary urea nitrogen results from the Kidney-Chek salivary test were correlated to BUN and creatinine results. This test can be used to detect azotemia in selected cases of feline urethral obstruction. Clinical relevance: This study demonstrated that SUN may be used to detect azotemia in cats at risk of urinary tract obstruction. This test may be useful for clients to use at home, before bringing their animals to the clinic, or as a cost-saving alternative in a clinic setting. Access to an immediately available azotemia reading can help practitioners make guided treatment recommendations and determine if cats are good candidates for outpatient treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".